Shanghai Jiao Tong University

About: Shanghai Jiao Tong University is a education organization based out in Shanghai, Shanghai, China. It is known for research contribution in the topics: Population & Cancer. The organization has 157524 authors who have published 184620 publications receiving 3451038 citations. The organization is also known as: Shanghai Communications University & Shanghai Jiaotong University.

Immunobiology of mesenchymal stem cells

Abstract: Mesenchymal stem cells (MSCs) can be isolated from almost all tissues and effectively expanded in vitro. Although their true in situ properties and biological functions remain to be elucidated, these in vitro expanded cells have been shown to possess potential to differentiate into specific cell lineages. It is speculated that MSCs in situ have important roles in tissue cellular homeostasis by replacing dead or dysfunctional cells. Recent studies have demonstrated that in vitro expanded MSCs of various origins have great capacity to modulate immune responses and change the progression of different inflammatory diseases. As tissue injuries are often accompanied by inflammation, inflammatory factors may provide cues to mobilize MSCs to tissue sites with damage. Before carrying out tissue repair functions, MSCs first prepare the microenvironment by modulating inflammatory processes and releasing various growth factors in response to the inflammation status. In this review, we focus on the crosstalk between MSCs and immune responses and their potential clinical applications, especially in inflammatory diseases.

Light-Triggered Theranostics Based on Photosensitizer-Conjugated Carbon Dots for Simultaneous Enhanced-Fluorescence Imaging and Photodynamic Therapy

Abstract: National Key Basic Research Program (973 Project) [2010CB933901, 2011CB933100]; National Natural Scientific Fund [51102258, 20803040, 81028009, 31170961]; New Century Excellent Talent of Ministry of Education of China [NCET-08-0350]; Shanghai Science and Technology Fund [1052nm04100]; Ministry of Education

RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems

Abstract: To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance. This paper considers the knowledge graph as the source of side information. To address the limitations of existing embedding-based and path-based methods for knowledge-graph-aware recommendation, we propose RippleNet, an end-to-end framework that naturally incorporates the knowledge graph into recommender systems. Similar to actual ripples propagating on the water, RippleNet stimulates the propagation of user preferences over the set of knowledge entities by automatically and iteratively extending a user's potential interests along links in the knowledge graph. The multiple "ripples" activated by a user's historically clicked items are thus superposed to form the preference distribution of the user with respect to a candidate item, which could be used for predicting the final clicking probability. Through extensive experiments on real-world datasets, we demonstrate that RippleNet achieves substantial gains in a variety of scenarios, including movie, book and news recommendation, over several state-of-the-art baselines.